A Web Service for Fragment-based Selectivity Analysis of Drug Leads

基于片段的先导药物选择性分析的 Web 服务

基本信息

批准号：
9906478
负责人：
John Laurence Kulp III
金额：
$ 23.47万
依托单位：
CONIFER POINT PHARMACEUTICALS, LLC
依托单位国家：
美国
项目类别：
财政年份：
2020
资助国家：
美国
起止时间：
2020-02-01 至 2022-09-09
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/9906478
关键词：
Address Adopted Affinity Algorithms Amino Acids Area Benchmarking Binding Binding Sites Chemicals Clinical Clinical Trials Cloud Service DHFR gene Data Data Set Drug Design Failure Family member Free Energy G-Protein-Coupled Receptors Goals Grant Hand Industry Internet Isoenzymes JAK2 gene JAK3 gene Journals Location Maps Measures Medicine Methodology Mutation Online Systems Outcome PTGS1 gene PTGS2 gene Patients Pattern Peer Review Peptide Hydrolases Pharmaceutical Preparations Phase Phosphotransferases Property Protein Family Protein Fragment Protein Isoforms Proteins Publications Publishing Research Personnel Resources Services Sirtuins Small Business Innovation Research Grant Software Tools Specificity Speed Technology Testing Therapeutic Toxic effect United States National Institutes of Health base clinical candidate design drug discovery enzyme pathway improved innovation lead optimization novel programs prototype repository side effect small molecule success tool web based interface web page web services web site

项目摘要

Abstract Significance: The goal of this proposal is to provide drug researchers with compelling new tools that address off-target selectivity in the rational design of drugs. To do this, we propose to employ our large repository of maps of where chemical fragments tightly bind to 1,000’s of proteins (Boltzmann maps) in searches for differ- ences in spatial binding patterns of fragments across protein families. This will empower medicinal chemists in attacking the off-target toxicity challenge that plagues clinical candidates in drug discovery. Our team is currently supported by an NIH Phase II SBIR grant (2R44GM109549) to computationally produce BMaps on a large scale (1,000’s of fragment maps on 1,000’s of proteins) for fragment-based drug design (see www.boltzmannmaps.com). Building upon this, the need is to build fast tools for the design of selective com- pounds that search these fragment maps in comparing binding patterns across a large number of proteins. Putting this capability in the hands of medicinal chemists across the industry via the Web has the potential to significantly improve those clinical outcomes negatively impacted by off-target toxicities and speed the delivery of new medicines to patients. Innovation: Comparing chemical fragment binding across a large number of diverse isozymes within protein families, based on our unique repository of fragment binding maps, is a new scientific approach to the rational design of selectivity. To efficiently query our large repository for binding patterns requires that we devise a new geometric search algorithm. Aim 1: Develop a novel algorithm for comparing fragment binding patterns using geometric hashing, supple- mented with other parameters such as the relative free energy from the chemical potential ranking and other physiochemical properties of the local binding site. We will provide a Web-based interface for requesting and visualizing search results. Aim 2: Validate the tools developed under Aim 1 in a proof-of-concept project to assess selectivity of a proprie- tary set of 127 compounds across the sirtuin family of proteins confirming experimental data. Overall Impact: In summary, having a body of diverse fragment binding data across isozymes within protein presents a unique opportunity to attack the selectivity problem in drug discovery. By comparing fragment in- teraction patterns across a large number of proteins, never feasible before, an assessment of differential bind- ing between proteins to modulate and proteins to leave unaffected is now more practical. As widely adopted, the service would provide a key tool to reduce toxicities due to off-target interactions, resulting in improved success rates of clinical trials.

抽象的意义：该提案的目的是为药物研究人员提供令人信服的新工具，以解决药物合理设计中的脱靶选择性。为此，我们建议使用我们的大型存储库化学片段的地图与1,000的蛋白质（Boltzmann地图）紧密结合，以搜索不同的 - 在蛋白质家族之间的碎片的空间结合模式中加密。这将赋予药物化学家的能力攻击困扰药物发现临床候选者的脱靶毒性挑战。我们的团队目前得到NIH II期SBIR赠款（2R44GM109549）的支持基于碎片的药物设计 www.boltzmannmaps.com）。在此基础上，需要建立快速工具，以设计选择性com- 在比较大量蛋白质的结合模式中搜索这些碎片图的磅。通过网络将这种能力掌握在整个行业中的药剂师手中，有可能显着改善那些受脱靶毒性产生负面影响并加快交付的临床结果为患者提供新药物。创新：比较蛋白质中大量潜水员的化学片段结合基于我们独特的碎片绑定图存储库的家庭是一种理性的新科学方法选择性设计。为了有效地查询我们的大存储库以确定绑定模式，我们需要设计一个新的几何搜索算法。 AIM 1：开发一种新型算法，用于使用几何哈希，柔软的片段结合模式进行比较用其他参数（例如化学势排名的相对自由能和其他参数）局部结合位点的生理化学特性。我们将提供一个基于Web的接口，用于请求和可视化搜索结果。目标2：验证AIM 1在概念验证项目中开发的工具，以评估所有权的选择性在蛋白质的Sirtuin家族中，有127种化合物确认实验数据。总体影响：总之，在蛋白质中具有跨同工酶的潜水员碎片结合数据提出了一个独特的机会来攻击药物发现中的选择性问题。通过比较碎片跨大量蛋白质的特性模式，以前从不可行，对差异结合的评估现在，蛋白质调节蛋白质与蛋白质不受影响的蛋白质现在更加实用。被广泛采用，该服务将为降低脱靶相互作用而降低毒性的关键工具，从而改善临床试验的成功率。